Real-time Optimal Operation of Microgrid with Power-to-hydrogen

2020 ◽  
Author(s):  
Zhiyao Zhong ◽  
Danji Huang ◽  
Kewei Hu ◽  
Xiaomeng Ai ◽  
Jiakun Fang
Keyword(s):  
Real Time ◽  
Optimal Operation ◽  
Time Optimal

scholarly journals Dynamic Modelling and Simulation of a Multistage Flash Desalination System

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 522
Author(s):  
Qiu-Yun Huang ◽  
Ai-Peng Jiang ◽  
Han-Yu Zhang ◽  
Jian Wang ◽  
Yu-Dong Xia ◽  
...  
Keyword(s):  
Real Time ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Dynamic Change ◽  
Seawater Temperature ◽  
Dynamic Modelling ◽  
Optimal Operation ◽  
Simultaneous Solution ◽  
Time Optimal

As the leading thermal desalination method, multistage flash (MSF) desalination plays an important role in obtaining freshwater. Its dynamic modeling and dynamic performance prediction are quite important for the optimal control, real-time optimal operation, maintenance, and fault diagnosis of MSF plants. In this study, a detailed mathematical model of the MSF system, based on the first principle and its treatment strategy, was established to obtain transient performance change quickly. Firstly, the whole MSF system was divided into four parts, which are brine heat exchanger, flashing stage room, mixed and split modulate, and physical parameter modulate. Secondly, based on mass, energy, and momentum conservation laws, the dynamic correlation equations were formulated and then put together for a simultaneous solution. Next, with the established model, the performance of a brine-recirculation (BR)-MSF plant with 16-stage flash chambers was simulated and compared for validation. Finally, with the validated model and the simultaneous solution method, dynamic simulation and analysis were carried out to respond to the dynamic change of feed seawater temperature, feed seawater concentration, recycle stream mass flow rate, and steam temperature. The dynamic response curves of TBT (top brine temperature), BBT (bottom brine temperature), the temperature of flashing brine at previous stages, and distillate mass flow rate at previous stages were obtained, which specifically reflect the dynamic characteristics of the system. The presented dynamic model and its treatment can provide better analysis for the real-time optimal operation and control of the MSF system to achieve lower operational cost and more stable freshwater quality.

Long-term versus Real-time Optimal Operation for Gate Regulation during Flood in Urban Drainage Systems

2018 ◽  
Vol 15 (8) ◽  
pp. 750-759 ◽  
Author(s):  
Fatemeh Jafari ◽  
S. Jamshid Mousavi ◽  
Jafar Yazdi ◽  
Joong Hoon Kim
Keyword(s):  
Real Time ◽  
Optimal Operation ◽  
Urban Drainage ◽  
Drainage Systems ◽  
Urban Drainage Systems ◽  
Time Optimal

Distributed Model-Predictive Real-Time Optimal Operation of a Network of Smart Microgrids

2019 ◽  
Vol 10 (3) ◽  
pp. 2833-2845 ◽  
Author(s):  
Kumar Utkarsh ◽  
Dipti Srinivasan ◽  
Anupam Trivedi ◽  
Wenjie Zhang ◽  
Thomas Reindl
Keyword(s):  
Real Time ◽  
Optimal Operation ◽  
Distributed Model ◽  
Time Optimal

scholarly journals Risk Analysis for Reservoir Real-Time Optimal Operation Using the Scenario Tree-Based Stochastic Optimization Method

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 606 ◽  
Author(s):  
Yimeng Sun ◽  
Feilin Zhu ◽  
Juan Chen ◽  
Jinshu Li
Keyword(s):  
Risk Analysis ◽  
Stochastic Optimization ◽  
Real Time ◽  
Flood Control ◽  
Forecast Accuracy ◽  
Optimization Method ◽  
Optimal Operation ◽  
Scenario Tree ◽  
Analysis Model ◽  
Time Optimal

The inherent uncertainty of inflow forecasts hinders the reservoir real-time optimal operation. This paper proposes a risk analysis model for reservoir real-time optimal operation using the scenario tree-based stochastic optimization method. We quantify the probability distribution of inflow forecast uncertainty by developing the relationship between two forecast accuracy metrics and the standard deviation of relative forecast error. An inflow scenario tree is generated via Monte Carlo simulation to represent the uncertain inflow forecasts. We establish a scenario tree-based stochastic optimization model to explicitly incorporate inflow forecast uncertainty into the stochastic optimization process. We develop a risk analysis model based on the principle of maximum entropy (POME) to evaluate the uncertainty propagation process from flood forecasts to optimal operation. We apply the proposed methodology to a flood control system in the Daduhe River Basin, China. In addition, numerical experiments are carried out to investigate the effect of two different forecast accuracy metrics and different forecast accuracy levels on reservoir optimal flood control operation as well as risk analysis. The results indicate that the proposed methods can provide decision-makers with valuable risk information for guiding reservoir real-time optimal operation and enable risk-informed decisions to be made with higher reliabilities.

scholarly journals A Real-Time Optimal Gate Operation Model for Urban Drainage Systems

10.29007/ktzw ◽  
2018 ◽  
Author(s):  
Fatemeh Jafari ◽  
S. Jamshid Mousavi ◽  
Joong Hoon Kim
Keyword(s):  
Real Time ◽  
Optimal Operation ◽  
Initial Solution ◽  
Urban Drainage ◽  
Operation Model ◽  
Time Model ◽  
Drainage Systems ◽  
The Real ◽  
Urban Drainage Systems ◽  
Time Optimal

Flooding is a phenomenon that endangers human being life and property. There are many structural and non-structural options that can be considered in order to reduce destructive effects of flooding. In this study, we propose a new methodology to enhance the performance of a real-time optimal operation model for flood mitigation in urban drainage systems. An online real-time model is developed as a simulation-optimization approach that leads to optimal operational policies based on the real-time rainfall information. Rainfall-runoff processes and hydraulic routing in the pipes are simulated by the EPA stormwater management model (SWMM) which is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance for assorted sets of operating policies. The initial solution in the real-time model is obtained by a long-term optimal operation model based on historical past flood events. The approach is validated by applying it to a portion of the urban drainage system in Tehran, the capital of Iran, consisting of a prototype network of pipes and detention reservoir equipped with controllable gates. Results show that the proposed strategy in introducing a reasonable initial solution to the real-time model can successfully enhance the performance of the model.

scholarly journals Adaptive forecast-based real-time optimal reservoir operations: application to Lake Urmia

2019 ◽  
Vol 21 (5) ◽  
pp. 908-924 ◽  
Author(s):  
Keyhan Gavahi ◽  
S. Jamshid Mousavi ◽  
Kumaraswamy Ponnambalam
Keyword(s):  
Real Time ◽  
Salt Lake ◽  
Optimal Operation ◽  
Operation Model ◽  
Lake Urmia ◽  
Monthly Data ◽  
Neuro Fuzzy ◽  
Time Optimal ◽  
Dynamic Updating

Abstract Boukan Dam reservoir is the largest infrastructure constructed on the Zarineh-Roud River regulating streamflow for different uses including supplying water to Lake Urmia, the second largest salt lake in the world. This paper presents a forecast-based adaptive real-time optimal operation model (ARTOM) for Boukan reservoir with the aim of maximizing releases feeding the lake while meeting other needs such as irrigation, industrial, and domestic uses. Adaptive neuro-fuzzy system-based inflow-to-reservoir forecasts are used in the ARTOM to determine optimal releases from the reservoir for future months up to the end of a year, but only the current period's release is applied. At the beginning of the next period, the forecasts are updated, and the procedure is repeated until the last period of the year. Additionally, the optimal terminal end-of-year reservoir storage volume is a dynamic updating input to the ARTOM, which is estimated from the results of a long-term reservoir operation optimization model. The ARTOM performance is tested against the last nine-year monthly data not utilized for training the forecast module. Results demonstrate that the ARTOM attains an objective function value very close to the best possible value that can ever be reached by utilizing an ideal operation model, benefiting from perfect foresight on future streamflows.

scholarly journals Tracking the Uncertainty Propagation Process Between Hydrological Forecasting and Reservoir Real-Time Optimal Operation

2021 ◽  
Author(s):  
Feilin Zhu ◽  
Ping-an Zhong ◽  
Bin Xu ◽  
Yufei Ma ◽  
Qingwen Lu ◽  
...  
Keyword(s):  
Stochastic Optimization ◽  
Real Time ◽  
Uncertainty Propagation ◽  
Temporal Correlation ◽  
Optimal Operation ◽  
Gaussian Copula ◽  
Propagation Process ◽  
Hydrological Forecasting ◽  
Copula Method ◽  
Time Optimal

Abstract The inherent uncertainty in hydrological forecasting poses a challenge for reservoir real-time optimal operation. In this paper, a stochastic framework is proposed to track the uncertainty propagation process between hydrological forecasting and reservoir operation. The framework simulates the comprehensive uncertainty of hydrological forecasts in the form of ensemble forecasts and scenario trees. Based on the derived analytic relationship between the performance metric Nash-Sutcliffe efficiency coefficient (NSE) and forecast uncertainty probability distribution, we use three methods (two are commonly used classical methods and one is the Gaussian copula method) simultaneously to generate inflow forecast ensembles. Compared with the two classical methods, the Gaussian copula method additionally takes into account the temporal correlation of reservoir inflows. Then, the neural gas method is employed to transform the generated ensembles into a scenario tree, which is further used as an input for reservoir stochastic optimization. To improve the adaptability to uncertainties in inflow forecasts, we establish a stochastic optimization model that optimizes the expectation of objective values over all scenarios. Meanwhile, we propose a parallel differential evolution (DE) algorithm based on parallel computing techniques for solving the stochastic optimization model efficiently. Risk assessment is performed to capture the uncertainty and corresponding risk associated with the reservoir optimal decision. The proposed framework is demonstrated in a flood control reservoir system in China. Furthermore, we conduct several numerical experiments to explore the effect of forecast uncertainty level and temporal correlation on reservoir real-time optimal operation. The results indicate that the temporal correlation of inflows must be considered in inflow stochastic simulation and reservoir stochastic optimization, otherwise the operational risk is likely to be overestimated or underestimated, thus leading to operation failures. Based on the risk simulation surface, reservoir operators can examine the robustness of operational decisions and thus make more reliable final decisions.

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